Project
Random Forest from Scratch
Hard
68 completions
~ 19 hours
4.3Consolidate your knowledge of the Random forest algorithm. Compare it against a standalone decision tree and check if your algorithm overfits when you increase the number of trees in it.
Provided by
JetBrains Academy
About
In this project, we will dive into implementing one of the most popular while simple enough ensemble algorithms — Random forest. Implement the entire algorithm from scratch using numpy. Test it on the titanic dataset from sklearn.
Training project
This project allows you to practice and strengthen your coding skills, helping you get ready for more advanced tasks ahead.
What you'll learn
Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:
Use the default decision tree from sklearn to get some initial quality values.
Implement the bootstrap algorithm for your Random forest.
Implement the .fit method to make it possible for your Random forest to train.
Implement the .predict method to make for your Random forest.
Use your Random forest to get predictions and compare them with the results from Stage 1.
Check whether your Random forest for overfitting when you increase the trees.
Reviews
6 days ago
I have learned how to create decision tree engine -Random Forest- from scratch. I used bootstrapping approach (method), i used DecisionTreeClassifier, .train_test_split(), accuracy_score() from scikit-sklearn library.Also i used .fit(), .precidt() methods from scikit-learn DecisionTreeClassifierI ...
Erich H
8 months ago
I learned the concept of random bootstrapping, decision trees in the Random Forest context, and understand the methods of simplifying decission trees to handle big data.
1 year ago
Basic concepts of numpy and machine learning. Implemented Random Forest and learned how it works.
4.3
Learners who completed this project within the Introduction to Data Science course rated it as follows: